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Big Data Institute, Li Ka Shing Centre for Health and Information Discovery, Old Road Campus, Headington, Oxford, OX3 7LF

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"Postdoctoral Researcher - Infectious disease modelling"

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Postdoctoral Researcher - Infectious disease modelling

Location

Big Data Institute, Li Ka Shing Centre for Health and Information Discovery, Old Road Campus, Headington, Oxford, OX3 7LF

We are seeking to appoint a Postdoctoral Researcher in Infectious Disease Modelling. This is an exciting opportunity to join the Centre for Tropical Medicine and Global Health, based at the University of Oxford. You will be working with Professor Déirdre Hollingsworth and the Disease Modelling Group, which aims to create dynamic mathematical modelling solutions to inform and support key global and local health policy decisions of diseases that predominantly affect the lowest income populations.

As part of this project, we are looking for an enthusiastic and highly motivated individual to join our multidisciplinary team working at the interface of epidemiology, data science, and public health policy. The successful candidate will develop and apply advanced mathematical and computational models to understand the transmission dynamics of infectious diseases and generate insights to inform decision-making. The role will involve analysis of large epidemiological datasets, development of models of co-infection (including parameter estimation, model calibration, validation, etc.) and close collaboration with researchers, clinicians, and public health partners.

Professor Hollingsworth’s group is a world leader in the development of statistical, mathematical, and computational models that inform disease dynamics, and in the translation of outputs to national and global health policy. The Disease Modelling Group has made significant contributions to the study of neglected tropical diseases (NTDs), COVID-19, HIV/AIDS and malaria. Research outputs range from analytical mathematical models through to large-scale individual-based simulation as well as statistics and Bayesian inference. This highly motivated, collaborative research group leads funded, international consortia in modelling, NTDs and vaccine impact.

It is essential that you hold a PhD/DPhil (or close to completion) in a quantitative research area such as mathematical modelling, computing or statistics, alongside sufficient specialist knowledge in infectious disease modelling to work within the established research programmes. You will have an ability to manage own academic research and associated activities, with excellent communication skills including the ability to communicate with a wide range of stakeholders, write for publication, present research proposals and results, and represent the research group at meetings. You will have expertise with at least one programming language used for research (e.g. Python, C++, C, Matlab, R, Java, Javascript, Fortran, Julia) and conversant with at least one more. Strong analytical and quantitative skills and excellent problem-solving abilities are also required.

Applications for this vacancy should be made online and you will need to upload a supporting statement and CV. Your supporting statement must explain how you meet each of the selection criteria for the post using examples of your skills and experience. Please restrict your documentation to your CV and supporting statement only. Any other documents will be requested at a later date.

This position is offered full-time (part-time hours considered, a minimum of 80 % FTE) on a fixed term contract until 30 November 2027 and is funded by the European Research Council (ERC).

Only applications received before 12 midday on 19 January 2026 will be considered. Please quote 184010 on all correspondence.

Contact Person: Bogdan Tiritelnicu
Vacancy ID: 184010
Closing Date & Time: 19-Jan-2026 12:00
Pay Scale: RESEARCH GRADE 7
Contact Email: recruitment@ndm.ox.ac.uk
Salary (£): Research Grade 7: Salary in range £39,424 - £40,514 per annum. This is inclusive of a pensionable Oxford University Weighting of £1,730 per year.

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